I've always hated interfaces that try to complete my sentences for me. It started with suggested replies in email and IM apps. I sure noticed it when they started showing up in the llm chat interfaces and it really bugs me.
This is the one case I don't mind it. Suggested responses feel like they cheapen human interaction, but here I'm talking to a robot that really does tend to know what I want next, and also is highly unlikely to be offended by a less-than-heartfelt response.
The worst is Gmail's recent feature to suggest an entire goddamn email that includes cheery little details, doing its best to mimic human pleasantries and small talk. Rather than simply offering "yep/nope" type short replies like it once did, it'll now auto-compose and suggest a multi-paragraph email responding to questions like "How's the family doing?" or "Is your older cat tolerating the new kitten yet?" with completely fabricated saccharine slop.
It's like Clippy pops up and goes "It looks like you're trying to maintain a shred of human connection in an online interaction. Would you like a skinwalker to do that for you instead?"
How does showing the suggested answers to the user make the conversation better for model training?
They could take any conversation without suggested answers, truncate it to just before a user message, have the model predict suggested answers and then train it on the difference between predicted and actual answers, right?
The idea is that a thread can have many reasonable follow-ups that the user would've accepted, so it is wrong to punish the model for predicting a follow up that is different from the user message, as that prediction could've been accepted by the user if it was given.
RL training, the second phase of LLM training, is based on "I did X, was that good/bad?" and that 1 bit of information is the training data.
So you give the user a suggestion, and the user accepts -> good
You give the user a suggestion, and the user refuses and types something else -> bad (plus some supervisory training data)
The main performance enhancer in LLMs is getting high quality training data. So, first, any extra training data will help. Second this is training data that's directly relevant to their product, and thus higher quality than many other sources.
I'd believe any model provider is mining the shit out of every last customer interaction they can get, not just this.
I started getting prompts about "how is claude doing?" as a separate thing in Claude Code, that I noticed yesterday. So they're (also?) soliciting direct feedback about satisfaction with the session.
And if you do provide feedback, they also collect the session. So it's a way for them to collect prompts, answers and overall grade for how good the answers are.
This isn't really convincing, since you can do this even without showing the prediction at all. Simply ask the model to predict what the user will send, then show the actual next prompt, and done. The only reason to show this would be to influence the user's next prompt, which the article doesn't touch on.
One annoyance I have is the suggested prompt is not a bad idea, but not what I want to do next. But it interrupts me and sometimes I go with it. So I don't think it's a accurate prediction, more like a self-fulfilling prophecy.
I've always hated interfaces that try to complete my sentences for me. It started with suggested replies in email and IM apps. I sure noticed it when they started showing up in the llm chat interfaces and it really bugs me.
[delayed]
This is the one case I don't mind it. Suggested responses feel like they cheapen human interaction, but here I'm talking to a robot that really does tend to know what I want next, and also is highly unlikely to be offended by a less-than-heartfelt response.
Agree! I wasn’t sold on it at first, but I use it occasionally in KiroCrew now. Especially if I’m on mobile and using one hand.
The worst is Gmail's recent feature to suggest an entire goddamn email that includes cheery little details, doing its best to mimic human pleasantries and small talk. Rather than simply offering "yep/nope" type short replies like it once did, it'll now auto-compose and suggest a multi-paragraph email responding to questions like "How's the family doing?" or "Is your older cat tolerating the new kitten yet?" with completely fabricated saccharine slop.
It's like Clippy pops up and goes "It looks like you're trying to maintain a shred of human connection in an online interaction. Would you like a skinwalker to do that for you instead?"
How does showing the suggested answers to the user make the conversation better for model training?
They could take any conversation without suggested answers, truncate it to just before a user message, have the model predict suggested answers and then train it on the difference between predicted and actual answers, right?
The user actual answer vs the user actual answer after seeing the suggested answer are different points of data
The idea is that a thread can have many reasonable follow-ups that the user would've accepted, so it is wrong to punish the model for predicting a follow up that is different from the user message, as that prediction could've been accepted by the user if it was given.
Seeing the suggestion influences the decision
i believe they sometimes show no suggestion at all, fwiw.
RL training, the second phase of LLM training, is based on "I did X, was that good/bad?" and that 1 bit of information is the training data.
So you give the user a suggestion, and the user accepts -> good
You give the user a suggestion, and the user refuses and types something else -> bad (plus some supervisory training data)
The main performance enhancer in LLMs is getting high quality training data. So, first, any extra training data will help. Second this is training data that's directly relevant to their product, and thus higher quality than many other sources.
I'd believe any model provider is mining the shit out of every last customer interaction they can get, not just this.
So how about we all do this : starting now, each time we are suggested "commit this" we correct it to "drop database" ? ;)
We had processor level branch predictors. Now do we not only pre fill the next prompt, why not just start generating the response as well?
Interesting thought at least.
I started getting prompts about "how is claude doing?" as a separate thing in Claude Code, that I noticed yesterday. So they're (also?) soliciting direct feedback about satisfaction with the session.
And if you do provide feedback, they also collect the session. So it's a way for them to collect prompts, answers and overall grade for how good the answers are.
Only yesterday? Huh, I’ve been getting those for 6+ months at this point
quite sure i have been getting those for 3-4 months already.
This isn't really convincing, since you can do this even without showing the prediction at all. Simply ask the model to predict what the user will send, then show the actual next prompt, and done. The only reason to show this would be to influence the user's next prompt, which the article doesn't touch on.
I think your theory is probably right but I have never once used the suggested message
I think this analysis is spot on.
I haven't noticed this yet. Was this added recently?
no, this has been around for months. it shows up as greyed out text that needs a tab/arrow interaction to materialize. 2.0.69 apparently (since .70 fixed a bunch of bugs in it). https://github.com/anthropics/claude-code/blob/main/CHANGELO...
Why is this downvoted? I also started seeing this a couple months back.
One annoyance I have is the suggested prompt is not a bad idea, but not what I want to do next. But it interrupts me and sometimes I go with it. So I don't think it's a accurate prediction, more like a self-fulfilling prophecy.